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Abstract

Describes the interactions between task characteristics and human agent interfaces in a team rendezvous route-planning task. The agents include an interface agent and two different task agents that perform similar tasks. The MokSAF (Mock modular Semi-Automated Forces) interface agent links an artificial intelligence (AI) route planning agent to a geographic information system (GIS). Through this agent, the user specifies a start and an end point, and describes the composition and characteristics of a military platoon. Two aided conditions and one non-aided condition were examined. In the first aided condition, an autonomous route-planning agent (RPA) determines a minimum-cost path between the specified end points. The user is allowed to define additional "intangible" constraints that describe situational or social information that should be considered when determining the route. In the second aided condition, a different agent, a cooperative RPA, uses the same knowledge of the terrain and cost functions available to the autonomous RPA, but restricts its search to paths within regions drawn by the user. In the unaided condition (the naive RPA), the user draws the route manually, then submits it to be tested against the terrain and cost functions for feasibility. Both aided conditions are superior to the control but differ in their relative effectiveness by scenario. In this paper, we examine the varieties of challenges faced by commanders in two scenarios and relate them to the differential effectiveness of the agents.